The Bayesian Information Criterion (BIC) is a model selection criterion that balances model fit and complexity by penalizing the number of parameters. It is particularly useful for comparing models with different numbers of parameters, with lower BIC values indicating a more preferred model under the assumption of a large sample size and that the true model is within the candidate set.