Derivative-free optimization (DFO) is the mathematical study of the optimization algorithms that do not use derivatives. One branch of DFO focuses on model-based DFO methods, where an approximation of the objective function is used to guide the optimization algorithm. Historically, model-based DFO has often assumed that the objective function is smooth, but unavailable analytically. However, recent progress has brought model-based DFO into the realm of nonsmooth optimization. In this chapter, we survey some of the progress of model-based DFO for nonsmooth optimization. We begin with some historical context on model-based DFO. From there, we discuss methods for constructing models of smooth functions and their accuracy. This leads to modelling techniques for nonsmooth functions and a discussion on several frameworks for model-based DFO for NSO. We conclude the paper with some of our opinions on profitable research directions in model-based DFO for NSO.