How is overfitting reduced when more examples are added? I've developed a model and it's overfitting. I want to understand whether adding more instances to my data will reduce overfitting by making the model weaker on the training data or by improving the model's ability on the validation data.

Above is a learning curve of my model. As you can see the model is doing well on the train but failing on validation. From my current understanding, adding more examples will reduce overfitting by making the learner less flexible which means the training curve will rise, thus I will have a high bias. What seems like a better solution in this scenario is to use a better model, but I just want to be sure I'm on the right line before I go ahead and collect more data.

The trend for the training is up, while for the validation it seems saturated. It does comparably well until 150 samples, but according to these graphs, it does not signal that it'll improve with data. However, data is small, so I think it's still a good option to obtain some more (if it's not expensive) and see what happens for the rest of the curve. At the same time, I'd try changing the model or regularizing it more.
