Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution. It works by finding the parameter values that make the observed data most probable. In other words, MLE seeks to maximize the likelihood function, which measures how likely the observed data is given certain parameter values. This technique is widely used in various fields, including economics, biology, and machine learning. MLE provides a way to fit models to data, allowing researchers to make inferences and predictions based on the estimated parameters. It is particularly useful when dealing with complex models and large datasets.