We study kernel estimation of highest-density regions (HDR). Our main contributions are two-fold. First, we derive a uniform-in- bandwidth asymptotic approximation to a risk that is appropriate for HDR estimation. This approximation is then used to derive a bandwidth selection rule for HDR estimation possessing attractive asymptotic properties. We also present the results of numerical stud- ies that illustrate the benefits of our theory and methodology.