The Schwarz Criterion, also known as the Bayesian Information Criterion (BIC), is a statistical tool used for model selection. It helps determine which model best explains a given set of data while penalizing for complexity. The criterion balances the goodness of fit with the number of parameters in the model, discouraging overfitting. In practice, lower values of the Schwarz Criterion indicate a better model. Researchers often compare the BIC values of different models to identify the most appropriate one for their data. This method is widely used in various fields, including econometrics and machine learning.