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 more parameters, thus discouraging overfitting. BIC is derived from the principles of Bayesian statistics and is particularly useful when dealing with large datasets. It provides a way to quantify the trade-off between the accuracy of a model and its simplicity, making it easier to choose the most appropriate model for the data at hand.