All of the references conclude this in some form or another. Hastie et al talk about bias v variance and how in general LOO has effectively zero bias but a lot of variance. Others talk about feature selection rather than classification/regression. Always the conclusion is that empirically, on average, you're better off using K Fold (for K in [5,10] than LOO. It might be strong to say, "LOO often results in poor estimation of the test error". What about "LOO tends to overfit the training data and has high variance as an estimator of the true error".