scikit-learn
diff --git a/‎sklearn/inspection/_permutation_importance.py
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diff --git a/‎sklearn/model_selection/_search.py
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diff --git a/‎sklearn/model_selection/_validation.py
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@@ -115,7 +115,7 @@ def permutation_importance(estimator, X, y, *, scoring=None, n_repeats=5,
         - a single string (see :ref:`scoring_parameter`);
         - a callable (see :ref:`scoring`) that returns a single value.
 
-        If `scoring` reprents multiple scores, one can use:
+        If `scoring` represents multiple scores, one can use:
 
         - a list or tuple of unique strings;
         - a callable returning a dictionary where the keys are the metric
 
@@ -1006,7 +1006,7 @@ class GridSearchCV(BaseSearchCV):
         - a single string (see :ref:`scoring_parameter`);
         - a callable (see :ref:`scoring`) that returns a single value.
 
-        If `scoring` reprents multiple scores, one can use:
+        If `scoring` represents multiple scores, one can use:
 
         - a list or tuple of unique strings;
         - a callable returning a dictionary where the keys are the metric
@@ -1346,7 +1346,7 @@ class RandomizedSearchCV(BaseSearchCV):
         - a single string (see :ref:`scoring_parameter`);
         - a callable (see :ref:`scoring`) that returns a single value.
 
-        If `scoring` reprents multiple scores, one can use:
+        If `scoring` represents multiple scores, one can use:
 
         - a list or tuple of unique strings;
         - a callable returning a dictionary where the keys are the metric
 
@@ -74,7 +74,7 @@ def cross_validate(estimator, X, y=None, *, groups=None, scoring=None, cv=None,
         - a single string (see :ref:`scoring_parameter`);
         - a callable (see :ref:`scoring`) that returns a single value.
 
-        If `scoring` reprents multiple scores, one can use:
+        If `scoring` represents multiple scores, one can use:
 
         - a list or tuple of unique strings;
         - a callable returning a dictionary where the keys are the metric