  Despite its impressive theory \& practical performance, Frequent Directions
(\acrshort{fd}) has not been widely adopted for large-scale regression tasks.
Prior work has shown randomized sketches (i) perform worse in estimating the
covariance matrix of the data than \acrshort{fd}; (ii) incur high error when
estimating the bias and/or variance on sketched ridge regression. We give the
first constant factor relative error bounds on the bias \& variance for
sketched ridge regression using \acrshort{fd}. We complement these statistical
results by showing that \acrshort{fd} can be used in the optimization setting
through an iterative scheme which yields high-accuracy solutions. This improves
on randomized approaches which need to compromise the need for a new sketch
every iteration with speed of convergence. In both settings, we also show using
\emph{Robust Frequent Directions} further enhances performance.
