Overfitting occurs when a machine learning model learns not only the underlying pattern of the training data but also noise and random fluctuations. This happens when the model is too complex or trained for too long, capturing details that do not generalize well to new, unseen data.

In practice, an overfitted model will perform exceptionally well on the training data but poorly on test or real-world data. This is because the model has memorized the training set rather than understanding the general trends. As a result, overfitting leads to low bias but high variance, meaning the model's predictions vary significantly when applied to different datasets.

To prevent overfitting, techniques such as cross-validation, regularization (like L1 or L2), pruning, and dropout in neural networks are employed. Additionally, using simpler models or collecting more training data can help achieve better generalization.

In essence, overfitting harms the model's ability to generalize beyond its training data, making it less effective for practical applications. When building models, it's essential to strike a balance between accuracy on training data and generalization to unseen data. This is crucial for successful outcomes in data science with machine learning projects.