Motivation Consider, for example, that we want to perform ordinary least squares (OLS) to find a line of best fit between the points $x_1, \ldots, x_N$ in $p$ dimensional Euclidean space and labels $y_1, \ldots, y_N$. When the number of predictors $p$ is large, it is possible to end up with a linear regression model with high variance (and hence higher than desirable prediction error). In addition, the resulting coefficients learned by the model may be harder to interpret than an alternative mo...