Information Criteria are statistical tools used to evaluate and compare different models based on their fit to data. They help researchers determine which model best explains the observed data while penalizing for complexity. Common examples include the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). These criteria balance goodness-of-fit with model simplicity, aiming to prevent overfitting. A lower value of an information criterion indicates a better model. By using Information Criteria, analysts can make informed decisions about which model to choose for predictions or understanding underlying patterns in the data.