This paper gives a review of cross-validation methods. The original applications in multiple linear regression are considered first. It is shown how predictive accuracy depends on sample size and the number of predictor variables. Both two-sample and single-sample cross-validation indices are investigated. The application of cross-validation methods to the analysis of moment structures is then justified. An equivalence of a single-sample cross-validation index and the Akaike information criterion is pointed out. It is seen that the optimal number of parameters suggested by both single-sample and two-sample cross-validation indices will depend on sample size. Copyright 2000 Academic Press.