This principle is normally used for model selection, based on the following formulation: which strikes a balance between model complexity and data fit, i.e., the highest-probability model that best explains the data is the best model overall.
the best model is not necessarily the model that fits the data best, as such a model may over-fit the data; the model itself must also have a high prior probability.