Standard cross validation (when you randomly split data into k blocks) is a wrong thing to do for time-series because in time-series you often have serial dependence, data are not iid.
For example, draw a random walk path, then cut-off one point in the middle, fit a regression on the rest and predict the value for the cut-off point - your prediction error will be small because cut-off point's value if very similar to neighbor values, it's not independent from them so cross-validation in this case will give you very optimistic estimate of prediction error.
Better thing to do to test a time-series regression is to run a moving 1-step-ahead prediction with fitting a regression on every step.
But even then, just calculating mean squared error is not good enough if your series aren't stationary - it's not very indicative. You may need to compare 1-step-ahead prediction error of your model and some simple reference model like "next step predicted value is the same as previous step realized value". This will indicate how good is you model out-of-sample compared to a reference.