The Bayesian Information Criterion (BIC) is a statistical tool used for model selection. It helps researchers compare different models by balancing goodness of fit with model complexity. A lower BIC value indicates a better model, as it penalizes models that have too many parameters, which can lead to overfitting. BIC is derived from the likelihood function and incorporates a penalty term based on the number of parameters in the model and the sample size. This makes it particularly useful in scenarios where multiple models are being evaluated, allowing for a more informed choice of the most appropriate model for the data.