Features in predictive models are not exchangeable, yet common supervised models treat them as such. Here we study ridge regression when the analyst can partition the features into $K$ groups based on external side-information. For example, in high-throughput biology, features may represent gene expression, protein abundance or clinical data and so each feature group represents a distinct modality. The analyst's goal is to choose optimal regularization parameters $λ= (λ_1, \dotsc, λ_K)$ -- one for each group. In this work, we study the impact of $λ$ on the predictive risk of group-regularized ridge regression by deriving limiting risk formulae under a high-dimensional random effects model with $p\asymp n$ as $n \to \infty$. Furthermore, we propose a data-driven method for choosing $λ$ that attains the optimal asymptotic risk: The key idea is to interpret the residual noise variance $σ^2$, as a regularization parameter to be chosen through cross-validation. An empirical Bayes construction maps the one-dimensional parameter $σ$ to the $K$-dimensional vector of regularization parameters, i.e., $σ\mapsto \widehatλ(σ)$. Beyond its theoretical optimality, the proposed method is practical and runs as fast as cross-validated ridge regression without feature groups ($K=1$).