# instantiate and fit the grid
grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False) grid.fit(X, y)
# view the results
pd.DataFrame(grid.cv_results_)[['mean_test_score', 'std_test_score', 'params']]

# Results
mean_test_score std_test_score  params
0   0.960000    0.053333    {'n_neighbors': 1, 'weights': 'uniform'}
1   0.960000    0.053333    {'n_neighbors': 1, 'weights': 'distance'}
2   0.953333    0.052068    {'n_neighbors': 2, 'weights': 'uniform'}
3   0.960000    0.053333    {'n_neighbors': 2, 'weights': 'distance'}
4   0.966667    0.044721    {'n_neighbors': 3, 'weights': 'uniform'}
5   0.966667    0.044721    {'n_neighbors': 3, 'weights': 'distance'}
6   0.966667    0.044721    {'n_neighbors': 4, 'weights': 'uniform'}
7   0.966667    0.044721    {'n_neighbors': 4, 'weights': 'distance'}
8   0.966667    0.044721    {'n_neighbors': 5, 'weights': 'uniform'}
9   0.966667    0.044721    {'n_neighbors': 5, 'weights': 'distance'}
10  0.966667    0.044721    {'n_neighbors': 6, 'weights': 'uniform'}
11  0.966667    0.044721    {'n_neighbors': 6, 'weights': 'distance'}
12  0.966667    0.044721    {'n_neighbors': 7, 'weights': 'uniform'}
13  0.966667    0.044721    {'n_neighbors': 7, 'weights': 'distance'}
14  0.966667    0.044721    {'n_neighbors': 8, 'weights': 'uniform'}
15  0.966667    0.044721    {'n_neighbors': 8, 'weights': 'distance'}
16  0.973333    0.032660    {'n_neighbors': 9, 'weights': 'uniform'}
17  0.973333    0.032660    {'n_neighbors': 9, 'weights': 'distance'}
18  0.966667    0.044721    {'n_neighbors': 10, 'weights': 'uniform'}