The Akaike Information Criterion (AIC) is a statistical tool used to compare different models and determine which one best explains a given set of data. It balances the goodness of fit of the model with its complexity, penalizing models that have too many parameters. A lower AIC value indicates a better model, making it easier to select the most appropriate one. AIC is particularly useful in fields like econometrics, biostatistics, and machine learning, where multiple models may be tested. By providing a systematic way to evaluate models, AIC helps researchers avoid overfitting and choose simpler, more generalizable solutions.