Why sklearn uses as default metric the minkowsky distance for the knn model?

Module 1. The Predictive Modeling Pipeline Fitting a scikit-learn model on numerical data
#1

Why sklearn uses as default metric the minkowsky distance for the knn model among the the other available metrics ?

#2

Let’s have a look at the other default parameters of this estimator:

In [1]: from sklearn.neighbors import KNeighborsClassifier

In [2]: KNeighborsClassifier().get_params()
Out[2]: 
{'algorithm': 'auto',
 'leaf_size': 30,
 'metric': 'minkowski',
 'metric_params': None,
 'n_jobs': None,
 'n_neighbors': 5,
 'p': 2,
 'weights': 'uniform'}

So the default value for the parameter p is 2 and Minkowsky with p=2 is the same as the Euclidean distance. So effectively the default metric of scikit-learn’s KNN is the usual Euclidean distance.