Let's say you want to model the behavior of Y = X1 + X2 and you know that this is the model you want to make. Whether or not that approximates the true relationship well is unknown. But since you want to be able to have coefficients that explain how Xi affects Y, you build a regression model. You don't plan on adding/subtracting predictors (since you don't have any additional data) and you don't plan on comparing this model with another (no other model allows for interpretation).
Does it make sense to still use sample splitting or cross validation? If you do cross validation, do you average the coefficients? Or could you just use your entire data set to train the model.