New algorithms based on convex programming are proposed for worst case system identification. The algorithms are optimal within a factor of two asymptotically. Further, model validation, or data consistency, is embedded in the identification process. Explicit worst case identification error bounds in the H(infinity) norm are also derived for both uniformly and nonuniformly spaced frequency response samples.