# n_iter controls the number of searches
rand = RandomizedSearchCV(knn, param_dist, cv=10, scoring='accuracy', n_iter=10, random_state=5, return_train_score=False)
rand.fit(X, y)
pd.DataFrame(rand.cv_results_)[['mean_test_score', 'std_test_score', 'params']]

#DataFrame

mean_test_score std_test_score  params
0   0.973333    0.032660    {'weights': 'distance', 'n_neighbors': 16}
1   0.966667    0.033333    {'weights': 'uniform', 'n_neighbors': 22}
2   0.980000    0.030551    {'weights': 'uniform', 'n_neighbors': 18}
3   0.966667    0.044721    {'weights': 'uniform', 'n_neighbors': 27}
4   0.953333    0.042687    {'weights': 'uniform', 'n_neighbors': 29}
5   0.973333    0.032660    {'weights': 'distance', 'n_neighbors': 10}
6   0.966667    0.044721    {'weights': 'distance', 'n_neighbors': 22}
7   0.973333    0.044222    {'weights': 'uniform', 'n_neighbors': 14}
8   0.973333    0.044222    {'weights': 'distance', 'n_neighbors': 12}
9   0.973333    0.032660    {'weights': 'uniform', 'n_neighbors': 15}