# Confusion on the computation of Leave One Out cross validation?

1) I was studying about cross-validation and have a bit of confusion here. I understand about the k-fold technique, where if you have 100 data and do 10 folds validation, you use the n=10 data for training and another n=90 for calculating the error (or maybe the other way round?

However, when you do leave on out, you are basically training on n=99 data, and calculating error on the n=1 data? how do this work? because you can't basically fit on one data? Or am I getting something wrong here?

2) Secondly, isn't fitting data with a higher degree of polynomial always yield less error?