**Cross-validation**, sometimes called **rotation estimation**,^{[1]}^{[2]}^{[3]} is a technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on one subset (called the*training set*), and validating the analysis on the other subset (called the *validation set* or *testing set*). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds.

http://en.wikipedia.org/wiki/Cross-validation_(statistics)

https://bigsnarf.wordpress.com/2013/01/04/overfitting/