Cross validation

Hello everybody...
Me and some friends have been looking at the cross validation topic in the Julian Izenman book. At page 122 there's a doubt that merged while reading. We were able to understand the 10-fold and 5-fold at bias and mean squared error, but on the leave-one-out we couldn't understand why the variance has an increment. Here's the citation of the book itself:
"As well as issues of computational complexity ,the difference between taking V = 5 or 10 and taking V = n is one of “bias versus variance.” The
leave-one-out rule yields an estimate of PE R that has low bias but high
variance (arising from the high degree of similarity between the leave-one-
out learning sets), whereas the 5–fold or 10–fold rule yields an estimate
of PE R with higher bias but lower mean squared error (and also lower
variance). "

Perhaps we misunderstood the text but, we would appreciate your help if that'd be the case.
Thanks in advanced.