The Bayesian Information Criterion (BIC) is a model selection criterion that balances model fit and complexity, penalizing models with more parameters to prevent overfitting. It is particularly useful in statistical models and machine learning for comparing different models, with lower BIC values indicating a better model fit relative to its complexity.