Drivers in different countries have different driving styles, drive different types of vehicles, and are subject to different traffic regulations. This means, models need to be adapted to the situations they are to describe by varying their parameters (calibration). Furthermore, it must be verified that this procedure is successful (validation). After introducing a general overview over the components of calibration (mathematical principles, microscopic and macroscopic measures of performance, goodness-of fit function, data, nonlinear optimization), we give hints on choosing the right components and how to run the simulation for the calibration task at hand. We explain the various calibration methods such as least squared errors, maximum likelihood and Bayesian calibration by means of example and also discuss the necessary data preparation. We also give a list of trajectory data that is open to the research community. Finally, we introduce validation techniques and point to interpretation pitfalls and the limits of the predictive power of models.