Mahalanobis distance is a measure used to determine the distance between a point and a distribution. Unlike the standard Euclidean distance, it accounts for the correlations of the data set and the variance of each variable. This makes it particularly useful in identifying outliers in multivariate data. The formula for Mahalanobis distance involves the covariance matrix of the data, allowing it to scale the distances based on the spread of the data. It is widely used in fields such as statistics, machine learning, and pattern recognition to improve the accuracy of classification and clustering tasks.