diff --git a/sklearn/model_selection/_validation.py b/sklearn/model_selection/_validation.py
index 928225ef7454e..e9b8cf7bac5b1 100644
--- a/sklearn/model_selection/_validation.py
+++ b/sklearn/model_selection/_validation.py
@@ -1539,7 +1539,7 @@ def validation_curve(estimator, X, y, *, param_name, param_range, groups=None,
 def _aggregate_score_dicts(scores):
     """Aggregate the list of dict to dict of np ndarray
 
-    The aggregated output of _fit_and_score will be a list of dict
+    The aggregated output of _aggregate_score_dicts will be a list of dict
     of form [{'prec': 0.1, 'acc':1.0}, {'prec': 0.1, 'acc':1.0}, ...]
     Convert it to a dict of array {'prec': np.array([0.1 ...]), ...}
 
@@ -1559,5 +1559,9 @@ def _aggregate_score_dicts(scores):
     {'a': array([1, 2, 3, 10]),
      'b': array([10, 2, 3, 10])}
     """
-    return {key: np.asarray([score[key] for score in scores])
-            for key in scores[0]}
+    return {
+        key: np.asarray([score[key] for score in scores])
+        if isinstance(scores[0][key], numbers.Number)
+        else [score[key] for score in scores]
+        for key in scores[0]
+    }
diff --git a/sklearn/model_selection/tests/test_validation.py b/sklearn/model_selection/tests/test_validation.py
index 00b11191d902a..1cb08cc13f767 100644
--- a/sklearn/model_selection/tests/test_validation.py
+++ b/sklearn/model_selection/tests/test_validation.py
@@ -356,6 +356,23 @@ def test_cross_validate_invalid_scoring_param():
                         cross_validate, SVC(), X, y, scoring="mse")
 
 
+def test_cross_validate_nested_estimator():
+    # Non-regression test to ensure that nested
+    # estimators are properly returned in a list
+    # https://github.com/scikit-learn/scikit-learn/pull/17745
+    (X, y) = load_iris(return_X_y=True)
+    pipeline = Pipeline([
+        ("imputer", SimpleImputer()),
+        ("classifier", MockClassifier()),
+    ])
+
+    results = cross_validate(pipeline, X, y, return_estimator=True)
+    estimators = results["estimator"]
+
+    assert isinstance(estimators, list)
+    assert all(isinstance(estimator, Pipeline) for estimator in estimators)
+
+
 def test_cross_validate():
     # Compute train and test mse/r2 scores
     cv = KFold()