What does a negative coefficient of determination mean for evaluating ridge regression?
A negative value means you're getting a terrible fit - which makes sense if you create a test set that doesn't have the same distribution as the training set. 

From the sklearn documentation:


  The coefficient $R^2$ is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.