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. Essentially, MLE seeks to maximize the likelihood function, which measures how likely the observed data is given certain parameter values. In practice, MLE is widely used in various fields, including economics, biology, and machine learning. By applying MLE, researchers can create models that best fit their data, allowing for better predictions and insights. This method is particularly useful when dealing with complex datasets and distributions.