scikit-learn
diff --git a/‎doc/whats_new/v0.20.rst‎
Lines changed: 9 additions & 3 deletions b/‎doc/whats_new/v0.20.rst‎
Lines changed: 9 additions & 3 deletions
diff --git a/‎sklearn/metrics/scorer.py‎
Lines changed: 5 additions & 0 deletions b/‎sklearn/metrics/scorer.py‎
Lines changed: 5 additions & 0 deletions
diff --git a/‎sklearn/model_selection/tests/test_search.py‎
Lines changed: 4 additions & 4 deletions b/‎sklearn/model_selection/tests/test_search.py‎
Lines changed: 4 additions & 4 deletions
@@ -91,11 +91,11 @@ Preprocessing
 
 - Added :class:`MICEImputer`, which is a strategy for imputing missing
   values by modeling each feature with missing values as a function of
-  other features in a round-robin fashion. :issue:`8478` by 
+  other features in a round-robin fashion. :issue:`8478` by
   :user:`Sergey Feldman <sergeyf>`.
 
-- Updated :class:`preprocessing.MinMaxScaler` to pass through NaN values. :issue:`10404` 
-  by :user:`Lucija Gregov <LucijaGregov>`. 
+- Updated :class:`preprocessing.MinMaxScaler` to pass through NaN values. :issue:`10404`
+  by :user:`Lucija Gregov <LucijaGregov>`.
 
 Model evaluation
 
@@ -414,6 +414,12 @@ Preprocessing
   ``inverse_transform`` on unseen labels. :issue:`9816` by :user:`Charlie Newey
   <newey01c>`.
 
+Model evaluation and meta-estimators
+
+- Add improved error message in :func:`model_selection.cross_val_score` when
+  multiple metrics are passed in ``scoring`` keyword.
+  :issue:`11006` by :user:`Ming Li <minggli>`.
+
 Datasets
 
 - Fixed a bug in :func:`dataset.load_boston` which had a wrong data point.
 
@@ -19,6 +19,7 @@
 # License: Simplified BSD
 
 from abc import ABCMeta, abstractmethod
+from collections import Iterable
 import warnings
 
 import numpy as np
@@ -300,6 +301,10 @@ def check_scoring(estimator, scoring=None, allow_none=False):
                 "If no scoring is specified, the estimator passed should "
                 "have a 'score' method. The estimator %r does not."
                 % estimator)
+    elif isinstance(scoring, Iterable):
+        raise ValueError("For evaluating multiple scores, use "
+                         "sklearn.model_selection.cross_validate instead. "
+                         "{0} was passed.".format(scoring))
     else:
         raise ValueError("scoring value should either be a callable, string or"
                          " None. %r was passed" % scoring)
 
@@ -1217,10 +1217,10 @@ def test_fit_grid_point():
             assert_equal(n_test_samples, test.size)
 
     # Should raise an error upon multimetric scorer
-    assert_raise_message(ValueError, "scoring value should either be a "
-                         "callable, string or None.", fit_grid_point, X, y,
-                         svc, params, train, test, {'score': scorer},
-                         verbose=True)
+    assert_raise_message(ValueError, "For evaluating multiple scores, use "
+                         "sklearn.model_selection.cross_validate instead.",
+                         fit_grid_point, X, y, svc, params, train, test,
+                         {'score': scorer}, verbose=True)
 
 
 def test_pickle():