Heteroscedasticity refers to the circumstance in which the variability of a variable is unequal across the range of values of a second variable that predicts it, often violating the assumptions of homoscedasticity in regression analysis. It can lead to inefficient estimates and invalid inference in statistical models, necessitating the use of robust standard errors or transformation techniques to address the issue.