Many novel and emerging risk factors exhibit a significant association with cardiovascular disease, but have not been found to improve risk prediction. Statistical criteria used to evaluate such models and markers have largely relied on the receiver operating characteristic curve, which is an insensitive measure of improvement. Recently, new methods have been developed based on risk reclassification, or changes in risk strata following use of a new marker or model. Associated measures based on both calibration and discrimination have been proposed. This review describes previous methods used to evaluate models as well as the newly developed methods to evaluate clinical utility.