Homoskedasticity refers to the assumption in regression analysis that the variance of the errors is constant across all levels of the independent variable(s). It is a crucial assumption for the validity of ordinary least squares (OLS) estimations, as violations can lead to inefficient estimates and affect hypothesis testing results.