An important property of Reservoir Computing, and signal processing techniques in general, is generalization and noise robustness. In tra jectory generation tasks, we don�t want that a small deviation leads to an instability. For forecasting and system identification we want to avoid over-fitting. In prior work on Reservoir Computing, the addition of noise to the dynamic reservoir tra jectory is generally used. In this work, we show that high-performing reservoirs can be trained using only the commonly used ridge regression. We experimentally validate these claims on two very different tasks: long-term, robust tra jectory generation and system identification of a heating tank with variable dead-time.