It's not a great reference, but in this notebook (look for cell starting with "Now let's compute RMSE using 10-fold x-validation") they add up the square errors (using a dot product) of all the predictions in all the cross validations, and then at the end divide by the number of predictions and square-root, i.e;
$\sqrt{\frac{1}{n} \sum_k{\sum_j{(y_{jk} - \hat{y_{jk}})^2}}}$
This makes the most sense to me, in the answer given by user1449306 the size of the folds would have an effect, which doesn't make sense?
To get to this from the list of RMSEs, they could each be squared and multiplied by the number of test points in each, then added together and divided by the total number of test points (and then squarerooted). Roland's comment is correct.