# Calculate euclidean distances for KNN and cross validation given a 99x16 10 folds

I'm trying to implement KNN classification with cross-validation implementation in python. The data consists of 10 folds of size 99x64, each with their corresponding label of size 99x1. Do I have to calculate distances row by row between each fold, for a resulting $$99x1$$ distance-vector for every $$k-1$$ testing fold? E.g. Let's say we have three folds, each of dimension $$[99x64]$$. Assuming we are testing on fold $$1$$ with folds 2, 3 being the validating datasets, I would end up with two distance-vectors of size $$[99x1]$$ since I'm calculating the distance between rows of each fold.