Lane changes are important in traffic flow operations. They cause differences in flow over lanes and determine in some cases the start of congestion. Whereas calibration and validation are commonly used with car-following models, this is not common practice with lane-change models. Even then, it is not clear what calibration and validation entails for probabilistic lane change models. Therefore, this article reviews methodologies to calibrate and validate probabilistic lane change models, both microscopically and macroscopically. A likelihood is often used in calibration, but does not intuitively show the quality of the model. An example showed that it is possible to have the model calibrated and validated with accurate parameters all having the same error in the validation as in the calibration, but the quality of the model was still bad. Using a likelihood ensures the stochastic effects are well captured, but the conclusion is that for validation purposes one can better use a measure which has physical interpretation and which gives a value indicating the quality of the model for the purpose for which it needs to be used.