Domain Adaptation is a technique in machine learning that helps models perform well on new, but related, data. When a model is trained on one type of data, it may struggle with different data that has variations in style, context, or distribution. Domain adaptation aims to bridge this gap, allowing the model to generalize better to the new domain. This process often involves adjusting the model or its training process to account for the differences between the original data and the new data. Techniques can include re-weighting samples, feature transformation, or using additional data from the new domain to improve performance.