This chapter explores the role of covariate adjustment in randomized clinical trials under the estimand framework. In recent years, there has been growing interest in this technique, particularly with the US Food and Drug Administration issuing guidance that underscores the need to differentiate between conditional and unconditional treatment effects. While these effects may be the same in linear models, they generally differ in other contexts—a distinction often overlooked in clinical trial practice. This chapter examines when and how to implement covariate adjustment in randomized controlled trials, focusing mainly on unconditional effects and emphasizing its role in increasing precision and statistical power by accounting for baseline covariates in the analysis. Various statistical methods, including analysis of covariance (ANCOVA), inverse probability weighting, and g-computation, are discussed. Practical considerations, such as handling missing data, managing small sample sizes, and available software, are also addressed.