Mean centering is a data preprocessing technique where the mean of each variable is subtracted from the data, centering the data around zero and often improving the interpretability and performance of statistical models. It is commonly used in regression analysis and principal component analysis to enhance the model's ability to detect patterns and relationships by reducing multicollinearity and aligning data on a common scale.