The cross-entropy method is a parametric technique performing adaptive importance sampling in Monte-Carlo methods. Information about the distribution being integrated is collected as samples are generated from a parametric sampling distribution. The parameters of this distribution are iteratively updated to minimize the cross-entropy between the sample of the distribution of interest and the sampling distribution. This versatile adaptive technique has found many applications in rare even simulation, combinatorial optimization and optimization of functions with multiple extrema. Through a series of use cases, I'll present a quick practioner's guide to using the cross-entropy method and will discuss common tricks and pitfalls.