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The term 'overfitting' refers to a scenario in which a neural network model learns the training data too well, capturing not only the inherent patterns but also the noise and outliers within that data. As a result, while the model performs with high accuracy on the training set, it struggles with unseen or validation data because it has generalized poorly. This occurs when the model becomes overly complex, leading to a situation where it fails to predict accurately on new, unseen examples due to the peculiarities it has memorized from the training data.

In contrast, an effective model should achieve a balance, where it maintains decent performance on both the training and validation datasets. The other options do not correctly reflect the concept of overfitting. For instance, a model performing well on unseen data indicates generalization, which is the opposite of overfitting. High bias and low variance suggest an underfitted model, which also contrasts with the behavior of an overfitted model. Lastly, having too few parameters typically leads to underfitting, where the model is unable to capture the complexity of the data. Thus, the definition provided aligns perfectly with the characteristics associated with overfitting in the context of neural networks.