Computational models support environmental regulatory activities by providing the regulator an ability to evaluate available knowledge, assess alternative regulations, and provide a framework to assess compliance. But all models face inherent uncertainties because human and natural systems are always more complex and heterogeneous than can be captured in a model. Here, we provide a summary discussion of the activities, findings, and recommendations of the National Research Council's Committee on Regulatory Environmental Models, a committee funded by the U.S. Environmental Protection Agency to provide guidance on the use of computational models in the regulatory process. Modeling is a difficult enterprise even outside the potentially adversarial regulatory environment. The demands grow when the regulatory requirements for accountability, transparency, public accessibility, and technical rigor are added to the challenges. Moreover, models cannot be validated (declared true) but instead should be evaluated with regard to their suitability as tools to address a specific question. The committee concluded that these characteristics make evaluation of a regulatory model more complex than simply comparing measurement data with model results. The evaluation also must balance the need for a model to be accurate with the need for a model to be reproducible, transparent, and useful for the regulatory decision at hand. Meeting these needs requires model evaluation to be applied over the “life cycle” of a regulatory model with an approach that includes different forms of peer review, uncertainty analysis, and extrapolation methods than those for nonregulatory models.