Heteroskedasticity refers to the phenomenon in regression analysis where the variability of the errors is not constant across all levels of an independent variable, potentially leading to inefficient estimates and invalid inference. It is crucial to detect and address heteroskedasticity to ensure the reliability of statistical models, often using methods such as robust standard errors or transforming variables.