Cross-validation is the statistical practice of partitioning a sample of data into subsets such that the analysis is initially performed on a single subset, while the other subset(s) are retained for subsequent use in confirming and validating the initial analysis.
The initial subset of data is called the training set; the other subset(s) are called validation or testing sets.
The theory of cross-validation was inaugurated by Seymour Geisser. It is important in guarding against testing hypotheses suggested by the data ("Type III error"), especially where further samples are hazardous, costly or impossible (uncomfortable science) to collect.
