Overfitting/Underfitting with Data set size In the below graph,

x-axis =&gt; Data set Size
y-axis =&gt; Cross validation Score



Red line is for Training Data

Green line is for Testing Data


In a tutorial that I'm referring to, the author says that the point where the red line and the green line overlap means,

Collecting more data is unlikely to increase the generalization
performance and we're in a region that we are likely to underfit the
data. Therefore it makes sense to try out with a model with more capacity

I cannot quite understand meaning of the bold phrase and how it happens.
Appreciate any help.
So, the underfitting means that you still have capacity for improving your learning while overfitting means that you have used a capacity more than needed for learning.

Green area is where testing error is rising i.e. you should continue providing capacity (either data points or model complexity) to gain better results. More green line goes, more flat it becomes i.e. you are reaching the point where the provided capacity (which is data) is enough and better to try providing the other type of capacity which is model complexity. 

If it does not improve your test score or even reduce it that means that the combination of Data-Complexity was somehow optimal and you can stop training.