In leave-one-out cross-validation (LOOCV), each of the training sets looks very similar to the others, differing in only one observation. When you want to estimate the test error, you take the average of the errors over the folds. That average has a high variance.   Is there a mathematical formula, visual, or intuitive way to understand why that average has a higher variance compared with the $k$-fold cross validation?
How do  methods compare in terms of model variance and bias?   My question is partly motivated by this thread: . The answer there suggests that models learned with leave-one-out cross-validation have higher variance than those learned with regular $K$-fold cross-validation, making leave-one-out CV a worse choice.  However, my intuition tells me that in leave-one-out CV one should see relatively lower variance between models than in the $K$-fold CV, since we are only shifting one data point across folds and therefore the training sets between folds overlap substantially.  Or going in the other direction, if $K$ is low in the $K$-fold CV, the training sets would be quite different across folds, and the resulting models are more likely to be different (hence higher variance).  If the above argument is right, why would models learned with leave-one-out CV have higher variance?
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