Homoscedasticity refers to the assumption that the variance of errors or disturbances in a regression model is constant across all levels of the independent variable(s). It is crucial for ensuring the validity of statistical tests and confidence intervals in linear regression analysis, as heteroscedasticity can lead to inefficient estimates and biased inference.