In situations where the validation is much more expensive or infeasible to perform, optimization practitioners must carefully consider whether they can "trust" the best in-sample predictions when the objective function is subject to large levels of noise. We think it is likely that in a reduced-noise environment, the predicted and validated outcomes will be better aligned.