diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 09a1fa2..63b9d6c 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -14,7 +14,7 @@ jobs:
 
       strategy:
         matrix:
-          python-version: ["3.10", "3.11", "3.12", "3.13", "3.14"]
+          python-version: ["3.11", "3.12", "3.13", "3.14"]
         fail-fast: true
 
       steps:
diff --git a/README.md b/README.md
index 40e2516..4443554 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,7 @@ panelsplit is a Python package designed to facilitate time series cross-validati
 
 ## Installation
 
-panelsplit is tested for compatibility with python versions >= 3.10. You can install panelsplit using pip:
+panelsplit is tested for compatibility with python versions >= 3.11. You can install panelsplit using pip:
 
 ```bash
 pip install panelsplit
diff --git a/panelsplit/metrics.py b/panelsplit/metrics.py
index 5a1ec97..cc61b86 100644
--- a/panelsplit/metrics.py
+++ b/panelsplit/metrics.py
@@ -1,37 +1,42 @@
-from .utils.validation import _safe_indexing
+"""
+Metrics that are equivalent their sklearn counterparts, except for the fact that they work with SequentialCVPipeline.
+"""
+
+# Standard library
+import warnings
 from inspect import signature
 from collections.abc import Iterable
 from functools import partial
-from sklearn.metrics._scorer import _MultimetricScorer
-from sklearn.utils._param_validation import (
-    validate_params,
-)
-from sklearn.metrics._scorer import _PassthroughScorer, _get_response_method_name
 from copy import deepcopy
-from sklearn.utils.validation import _check_response_method
-import warnings
-from sklearn.base import is_regressor
-from panelsplit.utils._response import _get_response_values
-from sklearn.utils.metadata_routing import (
-    _MetadataRequester,
-    _raise_for_params,
-    _routing_enabled,
-    MetadataRequest,
-)
-from .utils.typing import EstimatorLike, ArrayLike
-from numpy.typing import NDArray
 from typing import Callable, Optional, List, Union, Any, Dict
+
+# Third-party / typing
 from typing_extensions import Self
+from numpy.typing import NDArray
 
-# all the error scores:
+# Local package utilities
+from .utils.validation import _safe_indexing
+from .utils.typing import EstimatorLike, ArrayLike
+from panelsplit.utils._response import _get_response_values
+
+# sklearn public metrics (single consolidated import)
 from sklearn.metrics import (
     accuracy_score,
+    adjusted_mutual_info_score,
+    adjusted_rand_score,
     average_precision_score,
     balanced_accuracy_score,
     brier_score_loss,
     class_likelihood_ratios,
+    completeness_score,
     d2_absolute_error_score,
+    d2_brier_score,
+    d2_log_loss_score,
     explained_variance_score,
+    f1_score,
+    fowlkes_mallows_score,
+    jaccard_score,
+    homogeneity_score,
     log_loss,
     matthews_corrcoef,
     max_error,
@@ -42,24 +47,37 @@
     mean_squared_error,
     mean_squared_log_error,
     median_absolute_error,
+    mutual_info_score,
+    normalized_mutual_info_score,
+    precision_score,
+    rand_score,
     r2_score,
+    recall_score,
     roc_auc_score,
     root_mean_squared_error,
     root_mean_squared_log_error,
     top_k_accuracy_score,
-)
-from sklearn.metrics.cluster import (
-    adjusted_mutual_info_score,
-    adjusted_rand_score,
-    completeness_score,
-    fowlkes_mallows_score,
-    homogeneity_score,
-    mutual_info_score,
-    normalized_mutual_info_score,
-    rand_score,
     v_measure_score,
 )
 
+# sklearn internals / utilities (note: private APIs)
+from sklearn.metrics._scorer import (
+    _MultimetricScorer,
+    _PassthroughScorer,
+    _get_response_method_name,
+)
+from sklearn.utils._param_validation import validate_params
+from sklearn.utils.validation import _check_response_method
+from sklearn.base import is_regressor
+
+# metadata routing utilities (used by some sklearn internals)
+from sklearn.utils.metadata_routing import (
+    _MetadataRequester,
+    _raise_for_params,
+    _routing_enabled,
+    MetadataRequest,
+)
+
 
 def _get_idx_from_last_cv(estimator: EstimatorLike) -> Union[None, List[NDArray]]:
     """
@@ -88,14 +106,63 @@ def make_SequentialCV_scorer(
     greater_is_better: bool = True,
     **kwargs: Any,
 ) -> Callable[..., float]:
+    """
+    Make a SequentialCVPipeline-compatible scorer from a performance metric.
+
+    A scorer is a wrapper around an arbitrary metric or loss function that is called
+    with the signature `scorer(estimator, X, y_true, **kwargs)`.
+
+    The parameter `response_method` allows to specify which method of the estimator
+    should be used to feed the scoring/loss function.
+
+    Parameters
+    ----------
+    score_func : callable
+        Score function (or loss function) with signature
+        ``score_func(y, y_pred, **kwargs)``.
+
+    response_method : {"predict_proba", "decision_function", "predict"} or \
+            list/tuple of such str, default="predict"
+
+        Specifies the response method to use get prediction from an estimator
+        (i.e. :term:`predict_proba`, :term:`decision_function` or
+        :term:`predict`). Possible choices are:
+
+        - if `str`, it corresponds to the name to the method to return;
+        - if a list or tuple of `str`, it provides the method names in order of
+          preference. The method returned corresponds to the first method in
+          the list and which is implemented by `estimator`.
+
+    greater_is_better : bool, default=True
+        Whether `score_func` is a score function (default), meaning high is
+        good, or a loss function, meaning low is good. In the latter case, the
+        scorer object will sign-flip the outcome of the `score_func`.
+
+    **kwargs : additional arguments
+        Additional parameters to be passed to `score_func`.
+
+    Returns
+    -------
+    Callable
+        Callable object that returns a scalar score; greater is better.
+
+    Examples
+    --------
+    >>> from panelsplit.metrics import make_SequentialCV_scorer
+    >>> from sklearn.metrics import brier_score_loss
+    >>> brier_loss_scorer= make_SequentialCV_scorer(brier_score_loss, response_method='predict_proba', greater_is_better=False)
+
+    >>> from panelsplit.pipeline import SequentialCVPipeline
+    >>> from sklearn.ensemble import RandomForestClassifier
+    >>> from sklearn.datasets import load_iris
+    >>> X, y  = load_iris(return_X_y=True)
+    >>> p = SequentialCVPipeline(steps = [('rf', RandomForestClassifier())], cv_steps = [None])
+    >>> p.fit(X, y)
+    >>> brier_loss_scorer(p, X, y)
+    """
     sign = 1 if greater_is_better else -1
 
     if response_method is None:
-        warnings.warn(
-            "response_method=None is deprecated in version 1.6 and will be removed "
-            "in version 1.8. Leave it to its default value to avoid this warning.",
-            FutureWarning,
-        )
         response_method = "predict"
     elif response_method == "default":
         response_method = "predict"
@@ -158,7 +225,6 @@ def __init__(
         self._sign = sign
         self._kwargs = kwargs
         self._response_method = response_method
-        # TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)
         self._deprecation_msg = None
 
     def _get_pos_label(self) -> Optional[Any]:
@@ -170,7 +236,6 @@ def _get_pos_label(self) -> Optional[Any]:
         return None
 
     def _accept_sample_weight(self) -> bool:
-        # TODO(slep006): remove when metadata routing is the only way
         return "sample_weight" in signature(self._score_func).parameters
 
     def __repr__(self) -> str:
@@ -217,7 +282,6 @@ def __call__(
         float
             Score function applied to prediction of estimator on X.
         """
-        # TODO (1.8): remove in 1.8 (scoring="max_error" has been deprecated in 1.6)
         if self._deprecation_msg is not None:
             warnings.warn(
                 self._deprecation_msg, category=DeprecationWarning, stacklevel=2
@@ -314,6 +378,7 @@ def _score(
             X,
             pos_label=pos_label,
         )
+
         # make lookup dict for fast matching
         pred_dict = dict(zip(idx, y_pred))
 
@@ -340,6 +405,36 @@ def _score(
     prefer_skip_nested_validation=True,
 )
 def get_scorer(scoring: Union[str, Callable]) -> Any:
+    """
+    Get a scorer from string.
+
+    `sklearn.metrics.get_scorer_names` can be used to retrieve the names
+    of all available scorers.
+
+    Parameters
+    ----------
+    scoring : str, callable or None
+        Scoring method as string. If callable it is returned as is.
+        If None, returns None.
+
+    Returns
+    -------
+    callable
+        The scorer.
+
+    Notes
+    -----
+    When passed a string, this function always returns a copy of the scorer
+    object. Calling `get_scorer` twice for the same scorer results in two
+    separate scorer objects.
+
+    Examples
+    --------
+    >>> from panelsplit.metrics import get_scorer
+    >>> accuracy = get_scorer("accuracy")
+    >>> accuracy(classifier, X, y)
+    """
+
     if isinstance(scoring, str):
         try:
             scorer = deepcopy(_SCORERS[scoring])
@@ -489,7 +584,11 @@ def _check_multimetric_scoring(estimator: EstimatorLike, scoring: Iterable) -> A
 neg_mean_gamma_deviance_scorer = make_SequentialCV_scorer(
     mean_gamma_deviance, greater_is_better=False
 )
+# D^2 scorers (fraction of explained Brier / log-loss)
 d2_absolute_error_scorer = make_SequentialCV_scorer(d2_absolute_error_score)
+d2_brier_scorer = make_SequentialCV_scorer(d2_brier_score)
+d2_log_loss_scorer = make_SequentialCV_scorer(d2_log_loss_score)
+
 
 # Standard Classification Scores
 accuracy_scorer = make_SequentialCV_scorer(accuracy_score)
@@ -583,6 +682,8 @@ def negative_likelihood_ratio(y_true: NDArray, y_pred: NDArray) -> float:
     neg_mean_poisson_deviance=neg_mean_poisson_deviance_scorer,
     neg_mean_gamma_deviance=neg_mean_gamma_deviance_scorer,
     d2_absolute_error_score=d2_absolute_error_scorer,
+    d2_brier_score=d2_brier_scorer,
+    d2_log_loss_score=d2_log_loss_scorer,
     accuracy=accuracy_scorer,
     top_k_accuracy=top_k_accuracy_scorer,
     roc_auc=roc_auc_scorer,
@@ -607,3 +708,17 @@ def negative_likelihood_ratio(y_true: NDArray, y_pred: NDArray) -> float:
     normalized_mutual_info_score=normalized_mutual_info_scorer,
     fowlkes_mallows_score=fowlkes_mallows_scorer,
 )
+
+
+for name, metric in [
+    ("precision", precision_score),
+    ("recall", recall_score),
+    ("f1", f1_score),
+    ("jaccard", jaccard_score),
+]:
+    _SCORERS[name] = make_SequentialCV_scorer(metric, average="binary")
+    for average in ["macro", "micro", "samples", "weighted"]:
+        qualified_name = "{0}_{1}".format(name, average)
+        _SCORERS[qualified_name] = make_SequentialCV_scorer(
+            metric, pos_label=None, average=average
+        )
diff --git a/panelsplit/model_selection/model_selection.py b/panelsplit/model_selection/model_selection.py
index f064826..2818035 100644
--- a/panelsplit/model_selection/model_selection.py
+++ b/panelsplit/model_selection/model_selection.py
@@ -970,8 +970,8 @@ class GridSearch(BaseSearch):
 
         If `scoring` represents a single score, one can use:
 
-        - a single string (see :ref:`scoring_string_names`);
-        - a callable (see :ref:`scoring_callable`) that returns a single value;
+        - a single string (see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-string-names);
+        - a callable (see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-callable) that returns a single value;
         - `None`, the `estimator`'s default evaluation criterion is used.
 
         If `scoring` represents multiple scores, one can use:
@@ -981,7 +981,7 @@ class GridSearch(BaseSearch):
           names and the values are the metric scores;
         - a dictionary with metric names as keys and callables as values.
 
-        See :ref:`multimetric_grid_search` for an example.
+        See https://scikit-learn.org/stable/modules/grid_search.html#multimetric-grid-search for an example.
 
     n_jobs : int, default=None
         Number of jobs to run in parallel.
@@ -1057,6 +1057,19 @@ class GridSearch(BaseSearch):
         A dict with keys as column headers and values as columns, that can be
         imported into a pandas ``DataFrame``.
 
+        For an example of visualization and interpretation of GridSearch results,
+        see https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py.
+
+        NOTE
+
+        The key ``'params'`` is used to store a list of parameter
+        settings dicts for all the parameter candidates.
+
+        For multi-metric evaluation, the scores for all the scorers are
+        available in the ``cv_results_`` dict at the keys ending with that
+        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
+        above. ('split0_test_precision', 'mean_train_precision' etc.)
+
     best_estimator_ : estimator
         Estimator that was chosen by the search, i.e. estimator
         which gave highest score (or smallest loss if specified)
@@ -1157,11 +1170,11 @@ class GridSearch(BaseSearch):
     GridSearch(estimator=SVC(),
                  param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})
     >>> sorted(clf.cv_results_.keys())
-    ['mean_fit_time', 'mean_score_time', 'mean_test_score',...
+    ['mean_test_score',...
      'param_C', 'param_kernel', 'params',...
      'rank_test_score', 'split0_test_score',...
      'split2_test_score', ...
-     'std_fit_time', 'std_score_time', 'std_test_score']
+     'std_test_score']
     """
 
     _parameter_constraints: dict = {
@@ -1251,8 +1264,8 @@ class RandomizedSearch(BaseSearch):
 
         If `scoring` represents a single score, one can use:
 
-        - a single string (see :ref:`scoring_string_names`);
-        - a callable (see :ref:`scoring_callable`) that returns a single value;
+        - a single string (see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-string-names);
+        - a callable (see https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-callable) that returns a single value;
         - `None`, the `estimator`'s default evaluation criterion is used.
 
         If `scoring` represents multiple scores, one can use:
@@ -1262,7 +1275,7 @@ class RandomizedSearch(BaseSearch):
           names and the values are the metric scores;
         - a dictionary with metric names as keys and callables as values.
 
-        See :ref:`multimetric_grid_search` for an example.
+        See https://scikit-learn.org/stable/modules/grid_search.html#multimetric-grid-search for an example.
 
         If None, the estimator's score method is used.
 
@@ -1347,6 +1360,19 @@ class RandomizedSearch(BaseSearch):
         A dict with keys as column headers and values as columns, that can be
         imported into a pandas ``DataFrame``.
 
+        For an example of analysing ``cv_results_``,
+        see https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_stats.html#sphx-glr-auto-examples-model-selection-plot-grid-search-stats-py.
+
+        NOTE
+
+        The key ``'params'`` is used to store a list of parameter
+        settings dicts for all the parameter candidates.
+
+        For multi-metric evaluation, the scores for all the scorers are
+        available in the ``cv_results_`` dict at the keys ending with that
+        scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown
+        above. ('split0_test_precision', 'mean_train_precision' etc.)
+
     best_estimator_ : estimator
         Estimator that was chosen by the search, i.e. estimator
         which gave highest score (or smallest loss if specified)
diff --git a/panelsplit/pipeline.py b/panelsplit/pipeline.py
index a11a483..5e0e1b5 100644
--- a/panelsplit/pipeline.py
+++ b/panelsplit/pipeline.py
@@ -204,9 +204,9 @@ def _method(
 
         for step_idx, (name, transformer) in enumerate(self.steps, start=1):
             t_start = time.time()
-            not_final_step = total_steps != step_idx
+            is_final = step_idx == total_steps
             # Use 'transform' for intermediate steps and the provided method for the final step.
-            method = "transform" if not_final_step else method_name
+            method = "transform" if not is_final else method_name
             if fit:
                 # Initialize fitted_steps_ if not already done (for dynamic methods)
                 if not hasattr(self, "fitted_steps_"):
@@ -242,7 +242,8 @@ def _method(
                     fitted_model,
                     method,
                     current_output,
-                    y if not not_final_step else None,
+                    y if is_final else None,
+                    use_indices=(self.include_indices and is_final),
                 )
 
             _log_message(
@@ -538,6 +539,7 @@ def _apply_method_to_indices(
         List
             List of predictions or transformed values.
         """
+
         X_subset = self._subset(X, indices)
         y_subset = None if y is None else self._subset(y, indices)
         if isinstance(X_subset, np.ndarray) and X_subset.ndim == 1:
@@ -550,7 +552,7 @@ def _apply_method_to_indices(
         output = _call_method_with_correct_args(model, method_name, X_subset, y_subset)
         return output
 
-    def _fit_method_step(  # type: ignore
+    def _fit_method_step(  # ignore : type
         self,
         transformer: EstimatorLike,
         X: ArrayLike,
@@ -558,7 +560,8 @@ def _fit_method_step(  # type: ignore
         cv: Optional[Union[PanelSplit, Iterable]] = None,
         return_output: bool = True,
         method: str = "transform",
-    ):
+        use_indices: bool = False,
+    ) -> Tuple:
         """
         Fit one pipeline step and optionally transform the data.
 
@@ -593,7 +596,7 @@ def _fit_method_step(  # type: ignore
             fitted = (None, None, model)
             if return_output:
                 # return_group is not considered here as cv == None.
-                if self.include_indices:
+                if use_indices:
                     output = (
                         np.arange(len(X)),
                         _call_method_with_correct_args(model, method, X, y),
@@ -628,9 +631,7 @@ def _fit_method_step(  # type: ignore
                         )
                         self._append_indexed_output(idx_trans, train_idx, output_trans)
             if return_output:
-                output = _sort_and_combine(
-                    idx_trans, include_indices=self.include_indices
-                )
+                output = _sort_and_combine(idx_trans, include_indices=use_indices)
 
             fitted = folds_models  # type: ignore
         if return_output:
@@ -644,6 +645,7 @@ def _method_step(
         method_name: str,
         X: ArrayLike,
         y: Optional[ArrayLike] = None,
+        use_indices: bool = False,
     ) -> Union[NDArray, IntoDataFrame, IntoSeries, Tuple[NDArray, Any]]:
         """
         Apply a method of a fitted model (or models) to input X.
@@ -656,7 +658,7 @@ def _method_step(
                 output = _call_method_with_correct_args(
                     fitted_model[-1], method_name, X, y
                 )
-                if self.include_indices:
+                if self.include_indices and use_indices:
                     return np.arange(len(X)), output
                 else:
                     return output
@@ -679,9 +681,7 @@ def _method_step(
                     predictions_with_idx, train_idx, output_list
                 )
 
-        return _sort_and_combine(
-            predictions_with_idx, include_indices=self.include_indices
-        )
+        return _sort_and_combine(predictions_with_idx, include_indices=use_indices)
 
     def _fit(self, X: ArrayLike, y: Optional[ArrayLike] = None) -> ArrayLike:
         """
@@ -698,13 +698,14 @@ def _fit(self, X: ArrayLike, y: Optional[ArrayLike] = None) -> ArrayLike:
             if transformer is None or transformer == "passthrough":
                 self.fitted_steps_[name] = None
                 continue
-            not_final_step = not step_idx == total_steps
+            is_final = step_idx == total_steps
             X_current, fitted_model = self._fit_method_step(
                 transformer,
                 X_current,
                 y,
                 self.cv_steps[step_idx - 1],
-                return_output=not_final_step,
+                return_output=not is_final,
+                use_indices=is_final,  # enforce no indices for intermediate steps
             )
 
             self.fitted_steps_[name] = fitted_model
@@ -1041,6 +1042,11 @@ def classes_(self) -> Optional[NDArray]:
         Optional[NDArray]
             Array of class labels from the final classifier step, or None if unavailable.
 
+        Note
+        -------
+        Keep in mind that this aggregates all classes seen across all splits. Each split's
+        classifier may vary in its classes (e.g. in the case of few observations and high class imabalance.)
+
         Examples
         --------
         >>> pipeline.fit(X, y)
@@ -1057,18 +1063,44 @@ def classes_(self) -> Optional[NDArray]:
             raise NotFittedError(
                 f"No fitted model found for final step '{last_step_name}'."
             )
-        # Case A: cross-validated storage: dict with 'splits': [(test_idx, model), ...]
-        elif fitted_step[0] is not None:
-            [check_is_fitted(model) for _, _, model in fitted_step]
-            assert all([is_classifier(model) for _, _, model in fitted_step])
+
+        # Case A: cross-validated storage is a list of (train_idx, test_idx, model)
+        if isinstance(fitted_step, list) and fitted_step and fitted_step[0] is not None:
+            # ensure fitted and classifier
+            try:
+                for _, _, model in fitted_step:
+                    check_is_fitted(model)
+            except Exception:
+                # treat as absent (avoids raising NotFittedError during dir())
+                raise AttributeError(
+                    "Final estimator(s) not fitted or otherwise unavailable"
+                )
+
+            if not all(is_classifier(model) for _, _, model in fitted_step):
+                # Not a classifier -> attribute should be considered absent
+                raise AttributeError("Final estimator is not a classifier")
+
+            # collect classes_ from each fold
             return np.unique(
-                np.concatenate([model[-1].classes_ for model in fitted_step])
+                np.concatenate([model.classes_ for _, _, model in fitted_step])
             )
-        # Case B: direct estimator instance (non-CV)
-        else:
-            check_is_fitted(fitted_step[-1])
-            assert is_classifier(fitted_step[-1])
-            return getattr(fitted_step[-1], "classes_", None)
+
+        # Case B: non-CV: fitted_step is (None, None, model)
+        # defensive handling in case representation differs slightly
+        try:
+            model = fitted_step[-1]
+        except Exception:
+            raise AttributeError("Unexpected fitted-step format for final estimator")
+
+        try:
+            check_is_fitted(model)
+        except Exception:
+            raise AttributeError("Final estimator is not fitted")
+
+        if not is_classifier(model):
+            raise AttributeError("Final estimator is not a classifier")
+
+        return getattr(model, "classes_", None)
 
     def __sklearn_tags__(self) -> Tags:
         tags = super().__sklearn_tags__()
diff --git a/pyproject.toml b/pyproject.toml
index b700002..23b200a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,7 +7,7 @@ authors = [
 ]
 license = {text = "MIT"}
 readme = "README.md"
-requires-python = ">=3.10"
+requires-python = ">=3.11"
 classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
@@ -19,7 +19,7 @@ dependencies = [
     "narwhals>=1.42.1",
     "numpy>=1.21.0",
     "pandas>=1.3.0",
-    "scikit-learn>=0.24.2",
+    "scikit-learn>=1.8.0",
     "scipy>=1.10.1",
     "tqdm>=4.67.1",
     "typing-extensions>=4.13.2",
diff --git a/tests/test_metrics.py b/tests/test_metrics.py
index 97a8b4b..39d5b0d 100644
--- a/tests/test_metrics.py
+++ b/tests/test_metrics.py
@@ -3,10 +3,13 @@
 import pytest
 
 from sklearn.base import BaseEstimator
+from sklearn.metrics import get_scorer_names
+
 
 from panelsplit.metrics import (
     _get_idx_from_last_cv,
     make_SequentialCV_scorer,
+    get_scorer,
 )
 
 
@@ -137,3 +140,8 @@ def simple_metric(y_true, y_pred):
     # compute expected manually: fold 1 test idx [0,1] -> preds [1,1] vs y [1,0] => mean([True, False]) = 0.5
     assert pytest.approx(per_fold_scores[0]) == 0.5
     # fold 2 test idx [2]()
+
+
+def test_scorers_available():
+    scorers = get_scorer_names()
+    [get_scorer(s) for s in scorers]
diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index a817909..b942dac 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -4,7 +4,10 @@
 from sklearn.ensemble import RandomForestRegressor
 from panelsplit.pipeline import SequentialCVPipeline
 from panelsplit.cross_validation import PanelSplit
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.impute import SimpleImputer
 import pandas as pd
+from .df_generation import create_rf_friendly_dataset
 
 
 # Dummy transformer that doubles the input.
@@ -297,6 +300,24 @@ def test_score_order():
     assert where_is_first_instance_of_first_test_period == where_is_first_score
 
 
+def test_score_on_sequentialCVpipeline():
+    df = create_rf_friendly_dataset()
+    X = df[[col for col in df.columns if "X" in col]]
+    y = df["y_binary"]
+
+    ps = PanelSplit(df.year, n_splits=9)
+
+    pipe = SequentialCVPipeline(
+        [("imputer", SimpleImputer()), ("rf", RandomForestClassifier(n_estimators=10))],
+        cv_steps=[None, ps],
+        include_indices=False,
+    )
+
+    pipe.fit(X, y)
+    pipe.set_params(include_indices=False)
+    pipe.score(X, y)
+
+
 # %%
 # %%
 if __name__ == "__main__":
diff --git a/tests/test_search.py b/tests/test_search.py
index 5c3fcbb..cdf51ef 100644
--- a/tests/test_search.py
+++ b/tests/test_search.py
@@ -3,6 +3,7 @@
 import pytest
 
 from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.impute import SimpleImputer
 from sklearn.metrics import average_precision_score, roc_auc_score
 from panelsplit.metrics import get_scorer
 from sklearn.utils._param_validation import InvalidParameterError
@@ -255,6 +256,23 @@ def test_score_method_uses_refit_metric(self):
         reference = get_scorer("roc_auc")(gs.best_estimator_, self.X, self.y_binary)
         self.assertAlmostEqual(score_from_method, reference, places=7)
 
+    def test_pipeline_gridsearch_no_nan(self):
+        df = create_rf_friendly_dataset(n_samples=10)
+        X = df[[c for c in df.columns if "X" in c]]
+        y = df["y_binary"]
+        ps = PanelSplit(df.year, n_splits=2)
+        pipe = SequentialCVPipeline(
+            [
+                ("imputer", SimpleImputer()),
+                ("rf", RandomForestClassifier(n_estimators=10)),
+            ],
+            cv_steps=[None, ps],
+        )
+        gs = GridSearch(pipe, {"rf__n_estimators": [10, 20]}, scoring="accuracy")
+        # should not raise and should not produce all-nan results
+        gs.fit(X, y)
+        assert not np.all(np.isnan(gs.cv_results_["mean_test_score"]))
+
 
 if __name__ == "__main__":
     unittest.main()
diff --git a/tests/test_sequentialcvpipeline_indices.py b/tests/test_sequentialcvpipeline_indices.py
new file mode 100644
index 0000000..2bf1ccf
--- /dev/null
+++ b/tests/test_sequentialcvpipeline_indices.py
@@ -0,0 +1,148 @@
+# tests/test_sequentialcvpipeline_indices.py
+import numpy as np
+import pytest
+
+from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
+from sklearn.impute import SimpleImputer
+from sklearn.metrics import mean_squared_error
+
+from panelsplit.pipeline import SequentialCVPipeline
+from panelsplit.cross_validation import PanelSplit
+from panelsplit.metrics import neg_mean_squared_error_scorer as mse_scorer
+
+# Small toy dataset used across tests
+X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]], dtype=float)
+y_reg = np.array([1.0, 2.0, 3.0, 4.0])
+y_clf = np.array([0, 0, 1, 1])  # simple binary labels
+
+# PanelSplit used for CV variants
+ps = PanelSplit([1, 1, 2, 3], n_splits=2, include_first_train_in_test=True)
+
+
+@pytest.mark.parametrize(
+    "cv_steps",
+    [
+        ([None, None]),
+        ([ps, None]),
+        ([None, ps]),
+        ([ps, ps]),
+    ],
+)
+@pytest.mark.parametrize("include_indices", [False, True])
+def test_regressor_cv_variants(cv_steps, include_indices):
+    """
+    Ensure regressor pipeline fits, predicts, and scoring works for
+    different cv_steps and include_indices combinations.
+    """
+    p = SequentialCVPipeline(
+        steps=[
+            ("impute", SimpleImputer()),
+            ("regressor", RandomForestRegressor(random_state=0)),
+        ],
+        cv_steps=cv_steps,
+        include_indices=include_indices,
+    )
+
+    # fit should not raise
+    p.fit(X, y_reg)
+
+    # predict should return a 1D array of length n_samples
+    preds = p.predict(X)
+    if include_indices:
+        preds = preds[-1]
+
+    assert isinstance(preds, np.ndarray), "predict must return numpy array"
+    assert preds.shape[0] == X.shape[0], (
+        "predict must return prediction for each sample"
+    )
+
+    # predictions should not contain tuples (no tuple leaked into final preds)
+    # this checks that intermediate steps didn't produce (idx, data) that became final preds
+    flat = np.atleast_1d(preds)
+    assert not any(isinstance(v, tuple) for v in flat), (
+        "predictions must not contain tuples"
+    )
+
+    # scorer must run and return a finite float (scorer may internally set include_indices)
+    score = mse_scorer(p, X, y_reg)
+    # assert isinstance(score, float), "scorer must return a float"
+    # assert np.isfinite(score), "scorer result must be finite"
+    assert not any(np.isnan(score))
+
+    # additionally, compare with direct MSE of pipeline predictions (when cv_steps are None this must match)
+    # If the scorer uses CV predictions, they may differ; so only compare when cv_steps have no split
+    if all(s is None for s in cv_steps):
+        mse_direct = -mean_squared_error(y_reg, preds)
+        assert pytest.approx(mse_direct, rel=1e-6) == score[0]
+
+
+@pytest.mark.parametrize(
+    "cv_steps",
+    [
+        ([None, None]),
+        ([ps, None]),
+        ([None, ps]),
+        ([ps, ps]),
+    ],
+)
+@pytest.mark.parametrize("include_indices", [False, True])
+def test_classifier_cv_variants(cv_steps, include_indices):
+    """
+    Ensure classifier pipeline fits, predicts, returns classes_, and scoring works.
+    """
+    clf = RandomForestClassifier(random_state=0)
+
+    p = SequentialCVPipeline(
+        steps=[("impute", SimpleImputer()), ("classifier", clf)],
+        cv_steps=cv_steps,
+        include_indices=include_indices,
+    )
+
+    # fit must not raise
+    p.fit(X, y_clf)
+
+    # classes_ should be present and be ndarray for classifier
+    # (this property may raise for non-classifiers; for classifier it must succeed)
+    classes = p.classes_
+    assert isinstance(classes, np.ndarray), (
+        "classes_ must be an ndarray for a fitted classifier"
+    )
+    assert set(classes).issubset(set(y_clf)), (
+        "classes_ should reflect labels seen in training data"
+    )
+
+    # predict returns predictions with correct shape
+    preds = p.predict(X)
+    if include_indices:
+        preds = preds[-1]
+
+    assert preds.shape[0] == X.shape[0]
+    assert not any(isinstance(v, tuple) for v in np.atleast_1d(preds)), (
+        "predictions must not contain tuples"
+    )
+
+    # run a scorer that uses include_indices internally (we re-use the MSE scorer for shape & no-exception check)
+    # We expect a finite float (even though MSE scorer isn't conceptually for classification, it should still run)
+    score = mse_scorer(p, X, y_clf)
+    # assert isinstance(score, float)
+    # assert np.isfinite(score)
+    assert not any(np.isnan(score))
+
+
+def test_predict_and_scorer_consistency_for_no_cv():
+    """
+    Quick sanity check: when cv_steps = [None, None] the scorer's output must match
+    a direct negative MSE computed from p.predict(X).
+    """
+    p = SequentialCVPipeline(
+        steps=[
+            ("impute", SimpleImputer()),
+            ("regressor", RandomForestRegressor(random_state=0)),
+        ],
+        cv_steps=[None, None],
+        include_indices=False,
+    )
+    p.fit(X, y_reg)
+    preds = p.predict(X)
+    score = mse_scorer(p, X, y_reg)
+    assert pytest.approx(-mean_squared_error(y_reg, preds), rel=1e-9) == score[0]
diff --git a/uv.lock b/uv.lock
index a63c042..78e72e6 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,47 +1,15 @@
 version = 1
 revision = 1
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+requires-python = ">=3.11"
 resolution-markers = [
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-    "python_full_version == '3.11.*'",
-    "python_full_version == '3.10.*'",
-    "python_full_version == '3.9.*'",
-    "python_full_version < '3.9'",
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-
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+    "python_full_version < '3.12'",
 ]
 
 [[package]]
 name = "alabaster"
 version = "1.0.0"
 source = { registry = "https://pypi.org/simple" }
-resolution-markers = [
-    "python_full_version >= '3.12'",
-    "python_full_version == '3.11.*'",
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