When Does Survey-Aware Cross-Validation Matter? The ICC, Not the Design Effect
K-fold cross-validation assumes exchangeable observations, violated by the stratification, clustering, and unequal weights of complex sample surveys. Design-respecting"survey CV"exists, but the question of when the extra care changes any conclusion has remained open. We answer it before validating any model, with two inexpensive diagnostics: the within-cluster intraclass correlation (ICC) of the outcome and of a preliminary linear predictor. A simulated positive control demonstrates their sensitivity, with naive cross-validation growing optimistic about new-cluster performance as the ICC rises while cluster-level folds stay honest. We then evaluate paired naive-versus-design-respecting cross-validation in three national health surveys (chronic-pain, diabetes, and adolescent-suicidality prediction; penalized and random-forest learners, plus an unpenalized comparator) - one reanalysis, one prospective application, and one prespecified screen. In all three the diagnostics correctly anticipated the outcome: no scheme difference of practical size, and the only interval excluding zero showed pessimism, not the optimism that cluster leakage produces - even where the design effect was large (which, unlike the ICC, is not the right trigger). We also show the stratified recipe is often infeasible in public-use designs and give a fallback hierarchy, and we document weight-handling errors whose order-of-magnitude artifacts dwarfed any fold-scheme effect. Reproducible code accompanies the paper.