scikit-learn
diff --git a/‎doc/whats_new/v0.24.rst
Copy file name to clipboardExpand all lines: doc/whats_new/v0.24.rst
+7Lines changed: 7 additions & 0 deletions b/‎doc/whats_new/v0.24.rst
Copy file name to clipboardExpand all lines: doc/whats_new/v0.24.rst
+7Lines changed: 7 additions & 0 deletions
diff --git a/‎sklearn/model_selection/_validation.py
Copy file name to clipboardExpand all lines: sklearn/model_selection/_validation.py
+14-6Lines changed: 14 additions & 6 deletions b/‎sklearn/model_selection/_validation.py
Copy file name to clipboardExpand all lines: sklearn/model_selection/_validation.py
+14-6Lines changed: 14 additions & 6 deletions
diff --git a/‎sklearn/model_selection/tests/test_search.py
Copy file name to clipboardExpand all lines: sklearn/model_selection/tests/test_search.py
+19Lines changed: 19 additions & 0 deletions b/‎sklearn/model_selection/tests/test_search.py
Copy file name to clipboardExpand all lines: sklearn/model_selection/tests/test_search.py
+19Lines changed: 19 additions & 0 deletions
@@ -33,6 +33,13 @@ Changelog
   sample_weight object is not modified anymore. :pr:`19182` by
   :user:`Yosuke KOBAYASHI <m7142yosuke>`.
 
+:mod:`sklearn.model_selection`
+..............................
+
+- |Fix| :class:`model_selection.RandomizedSearchCV` and
+  :class:`model_selection.GridSearchCV` now correctly shows the score for
+  single metrics and verbose > 2. :pr:`19659` by `Thomas Fan`_.
+
 :mod:`sklearn.preprocessing`
 ............................
 
 
@@ -631,13 +631,21 @@ def _fit_and_score(estimator, X, y, scorer, train, test, verbose,
         total_time = score_time + fit_time
         end_msg = f"[CV{progress_msg}] END "
         result_msg = params_msg + (";" if params_msg else "")
-        if verbose > 2 and isinstance(test_scores, dict):
-            for scorer_name in sorted(test_scores):
-                result_msg += f" {scorer_name}: ("
+        if verbose > 2:
+            if isinstance(test_scores, dict):
+                for scorer_name in sorted(test_scores):
+                    result_msg += f" {scorer_name}: ("
+                    if return_train_score:
+                        scorer_scores = train_scores[scorer_name]
+                        result_msg += f"train={scorer_scores:.3f}, "
+                    result_msg += f"test={test_scores[scorer_name]:.3f})"
+            else:
+                result_msg += ", score="
                 if return_train_score:
-                    scorer_scores = train_scores[scorer_name]
-                    result_msg += f"train={scorer_scores:.3f}, "
-                result_msg += f"test={test_scores[scorer_name]:.3f})"
+                    result_msg += (f"(train={train_scores:.3f}, "
+                                   f"test={test_scores:.3f})")
+                else:
+                    result_msg += f"{test_scores:.3f}"
         result_msg += f" total time={logger.short_format_time(total_time)}"
 
         # Right align the result_msg
 
@@ -2137,3 +2137,22 @@ def test_search_cv_using_minimal_compatible_estimator(SearchCV, Predictor):
     else:
         assert_allclose(y_pred, y.mean())
         assert search.score(X, y) == pytest.approx(r2_score(y, y_pred))
+
+
+@pytest.mark.parametrize("return_train_score", [True, False])
+def test_search_cv_verbose_3(capsys, return_train_score):
+    """Check that search cv with verbose>2 shows the score for single
+    metrics. non-regression test fo #19658."""
+    X, y = make_classification(n_samples=100, n_classes=2, flip_y=.2,
+                               random_state=0)
+    clf = LinearSVC(random_state=0)
+    grid = {'C': [.1]}
+
+    GridSearchCV(clf, grid, scoring='accuracy', verbose=3, cv=3,
+                 return_train_score=return_train_score).fit(X, y)
+    captured = capsys.readouterr().out
+    if return_train_score:
+        match = re.findall(r"score=\(train=[\d\.]+, test=[\d.]+\)", captured)
+    else:
+        match = re.findall(r"score=[\d\.]+", captured)
+    assert len(match) == 3