There's more...

You could have just as easily run a simple loop to score the function more quickly:

all_scores = []
for n_neighbors in range(3,9,1):
    knn_clf = KNeighborsClassifier(n_neighbors = n_neighbors)
    all_scores.append((n_neighbors, cross_val_score(knn_clf, X_train, y_train, cv=10).mean()))
sorted(all_scores, key = lambda x:x[1], reverse = True)  

Its output suggests that n_neighbors = 4 is a good choice:

[(4, 0.85111111111111115),
 (7, 0.82611111111111113),
 (6, 0.82333333333333347),
 (5, 0.80666666666666664),
 (3, 0.79833333333333334),
 (8, 0.79833333333333334)]