Scikit-learn is a widely used, open-source machine learning library for Python. It’s built on two other Python packages: NumPy (Numerical Python) and SciPy (Scientific Python). Scikit-learn provides a wide range of supervised and unsupervised learning algorithms via a consistent interface.

Here are some key features of scikit-learn:

1. Supervised learning algorithms: Scikit-learn supports a wide range of supervised learning algorithms, including linear regression, support vector machines (SVMs), and decision trees, among others. These algorithms can be used for a variety of tasks including classification, regression, and outlier detection.

2. Unsupervised learning algorithms: Unsupervised learning is a type of machine learning where the model learns from data without any guidance. Examples of unsupervised learning algorithms available in scikit-learn include clustering (like K-means and hierarchical clustering), matrix factorization, and manifold learning.

3. Model selection and evaluation: Scikit-learn provides several ways to help you choose between models. For example, it provides tools for cross-validation, which allows you to estimate the performance of your model. It also provides tools for tuning hyperparameters, which are parameters that are set before the learning process begins.

4. Data preprocessing: Preprocessing data is a key step in many machine learning workflows. Scikit-learn provides tools for many common preprocessing tasks such as feature extraction, normalization, and encoding categorical variables.

5. Dimensionality reduction: Scikit-learn provides methods for reducing the number of random variables to consider, which can be important when dealing with high-dimensional data.

6. Ensemble methods: Scikit-learn also provides ensemble methods, which combine the predictions of multiple base estimators in order to improve generalizability/robustness over a single estimator. Examples include Random Forests and Gradient Boosting.

7. Pipeline: The Pipeline tool chains together many steps that can be cross-validated together while setting different parameters. It makes code easier to write and understand.

8. Multidataset Supervised Learning: Multioutput-Multiclass Classification and Multioutput Regression allow you to handle data that is not suitable for standard multioutput regression.

Here’s an example of how to use scikit-learn to train a simple linear regression model:

python
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn import datasets

# load the diabetes dataset (for regression)
diabetes = datasets.load_diabetes()

# split the data into training and test sets
X_train, X_test, Y_train, Y_test = train_test_split(diabetes.data, diabetes.target, test_size=0.2, random_state=42)

# create a linear regression model
model = LinearRegression()

# train the model
model.fit(X_train, Y_train)

# make predictions on the test set
predictions = model.predict(X_test)

Scikit-learn has excellent documentation and many tutorials available online, so it’s a great library to get started with if you’re new to machine learning. However, it’s also powerful and flexible enough for use in professional settings and research.