Latest article or new development in cross validation? Could anybody provide me a latest material related to Cross validation especially R package?
 A: The following is a recent survey: "A survey of cross-validation procedures for model selection" by Sylvain Arlot and Alain Celisse (Statistics Surveys, Volume 4 (2010), 40-79.)
The full paper can be downloaded by following the PDF link on this page.

Abstract
Used to estimate the risk of an estimator or to perform model selection, cross-validation is a widespread strategy because of its simplicity and its (apparent) universality. Many results exist on model selection performances of cross-validation procedures. This survey intends to relate these results to the most recent advances of model selection theory, with a particular emphasis on distinguishing empirical statements from rigorous theoretical results. As a conclusion, guidelines are provided for choosing the best cross-validation procedure according to the particular features of the problem in hand.

A: Your question is not really precise, but I think the caret package and its associated vignettes may be a good start. Quoting the website, it is

a set of functions that attempt to
  streamline the process for creating
  predictive models.

In fact, it depends on a lot of other R packages dedicated to ML (see the list of suggested packages on CRAN), but it definitively simplifies the management of cross-validation scheme (k-fold, leave-one-out).
The Elements of Statistical Learning (Hastie et al.) is available on-line in its second edition, and all illustrations are done in R. Cross-validation is described at length in Chapter 7.
