As we know, when deploying robots in the real world, the predictions from a robot's models often deviate from what actually occurs. In this talk, we will explore ways to use models more effectively for planning, regardless of their structure, by using data. First, we will examine model preconditions, which specify the conditions in which a model should be used, and one method to define them by predicting model deviation. Then, we will review some results that evaluate how characterizing model deviation can improve planning efficiency, reliability during execution, and data-efficiency capability expansion when current models are insufficient.  Finally, we will explore ongoing collaborative research on adapting state representation fidelity based on the planning problem. We test these methods in the real world where models are rarely accurate such as robot plant watering.