Why training and validation similar loss curves lead to poor performance
to prevent overfitting in a model the training curve in a loss graph
  should be similar to the validation curve.   


That's not always the case, maybe the train set has slightly a different distribution from the validation set, therefore, our focus is just when training loss decreases while validation loss slightly or never decreases.  

In the former graph, you notice there was a slight increase in the validation error while the training error was decreasing, while the latter graph has the same behavior due to a less complex model, the model was not able to fit the training data well.