We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy. A ‘robust’ classifier obtained via specialized techniques like removing spurious features has better OOD but worse ID accuracy compared to a ‘standard’ classifier trained via vanilla ERM. On six distribution shift datasets, we find that simply ensembling the standard and robust models is a strong baseline---we match the ID accuracy of a standard model with only a small drop in OOD accuracy compared to the robust model. However, calibrating these models in-domain surprisingly improves the OOD accuracy of the ensemble and completely eliminates the tradeoff and we achieve the best of both ID and OOD accuracy over the original models.