It might work well, it might not. When you add data to your training run, the model will converge differently because it sees different data and optimizes accordingly. This might be good or bad (it is not a given that it will deterministically end up being better simply because you gave it more data), but the problem is that you simply cannot tell because you have no held-out set anymore. If you do wish to squeeze everything out of your data, cross validation is recommended.