finding optimal k for knn using bootstrapping in sklearn

I can find optimal number of neighbors for knn using cross-validation using

grid = GridSearchCV(KNeighborsRegressor(), param_grid,  cv=10, scoring='neg_mean_squared_error')
grid.fit(X_train, y_train)

Is there a (quick) way to find the best k using bootstrapping instead of cross-validation?