Given normalized vectors, it's also proportional to the square of the Euclidian distance. Meaning for ranking (and thus k-nearest-neighbors) it doesn't matter if you use Euclidian distance or cosine similarity, you get the same result.

It isn't equivalent to it. See my other comment.