Statistical models are typically only accurate for a single distribution and will fail when fundamentals change. For example, a model that predicts self-diffusion coefficients will do a very poor job if it is only trained on liquids and tested on solids. This would be a stark example; in many scenarios a change in the underlying distribution is much less obvious than a phase change.