This article establishes a comprehensive theory of the optimality, robustness, and cross-validation selection consistency for ridge regression under factor-augmented models with possibly dense idiosyncratic information. Using spectral analysis random matrices, we show that is asymptotically efficient in capturing both factor information by minimizing limiting predictive loss among entire class ...