How to implement cross validation (on rolling forecasting origin) using ARIMA?

Suppose that I have a time-series dataset using 90% as training set and 10% as a random validation set. How do I evaluate the accuracy of an ARIMA model?

Do I have to fit the ARIMA model with auto.arima using the 100% of the full dataset and iteratively refit it to the training set using forecast::Arima to predict the validation set?

OR

Do I have to iteratively fit the ARIMA model with auto.arima using the training set and predict the validation set, and thus different model and no refitting each time?

I always thought that it was the first one, however, my model is doing weird things when doing so using Fourier terms to incorporate multiple seasonality.

Would be really appreciated if someone could help me out.