A property of diagnostic tests and risk models deserving more attention is risk stratification, defined as the ability of a test or model to separate those at high absolute risk of disease from those at low absolute risk. Risk stratification fills a gap between measures of classification (i.e. AUC) that do not require absolute risks and decision-analysis that requires not only absolute risks but also subjective specification of costs and utilities. We introduce Mean Risk Stratification (MRS) as the average change in risk of disease (posttest-pretest) revealed by a diagnostic test or risk model dichotomized at a risk threshold. MRS is particularly valuable for rare conditions, where AUC can be high but MRS can be low, identifying situations that temper overenthusiasm for screening with the new test/model. We apply MRS to the controversy over who should get testing for mutations in BRCA1/2 that cause high risks of breast and ovarian cancers. The value of MRS is to interpret AUC in the context of BRCA1/2 mutation prevalence and to provide a range of risk thresholds at which a risk model is "optimally informative".